Scholarlink

<p>This study presents an innovative method for landslide detection using a boosted cascade of simple features within a machine learning framework to efficiently and accurately identify areas susceptible to landslides. By leveraging AdaBoost algorithm-enhanced decision trees, this method analyzes terrain features from hillshade extracted from Digital Terrain Models (DTMs). The southwestern part of Cyprus, characterized by diverse geological-geotechnical conditions and rugged geomorphology, serves as the test area. This study evaluated the effectiveness of various DTM resolutions and cell sizes in detecting different types of landslides: active, dormant, and relict. The findings indicate that high-resolution DTMs and smaller cell sizes are most effective for identifying relict landslides with subtle features, albeit at high computational costs. For active landslides with steeper geomorphologies, a coarser cell size combined with moderate-resolution DTMs achieves an 80% success rate, significantly reducing computational time.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging artificial intelligence and digital terrain models for active and inactive landslide detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

查看全文
  • Ploutarchos Tzampoglou,
  • Dimitrios Loukidis,
  • Konstantinos Karalis,
  • Elena Valari,
  • Aristodemos Anastasiades,
  • Paraskevas Tsangaratos

摘要

This study presents an innovative method for landslide detection using a boosted cascade of simple features within a machine learning framework to efficiently and accurately identify areas susceptible to landslides. By leveraging AdaBoost algorithm-enhanced decision trees, this method analyzes terrain features from hillshade extracted from Digital Terrain Models (DTMs). The southwestern part of Cyprus, characterized by diverse geological-geotechnical conditions and rugged geomorphology, serves as the test area. This study evaluated the effectiveness of various DTM resolutions and cell sizes in detecting different types of landslides: active, dormant, and relict. The findings indicate that high-resolution DTMs and smaller cell sizes are most effective for identifying relict landslides with subtle features, albeit at high computational costs. For active landslides with steeper geomorphologies, a coarser cell size combined with moderate-resolution DTMs achieves an 80% success rate, significantly reducing computational time.